Method and system of computer-aided detection using multiple images from different views of a region of interest to improve detection accuracy

ABSTRACT

A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

CROSS-REFERENCE TO RELATED APPLICATION

This Patent Application claims priority to U.S. Patent Application Ser.No. 62/377,945 filed Aug. 22, 2016, the entire contents of which isherein incorporated by reference in its entirety.

BACKGROUND 1. Field

The present inventive concept relates generally to the field ofcomputer-aided detection of medical images and the detection ofsuspicious abnormalities or lesions. In particular, the presentinventive concept relates to a method and system for processing medicalimages which uses optical flow and block matching methods to determinethe persistence of a potential lesion detected over multiple imagescollected from different viewpoints and over a period of time.

2. Background

Computer-Aided Diagnosis (CAD), sometimes referred to as CADe or CADx,is used in the diagnosis of abnormal brain, breast cancer, lung cancer,colon cancer, prostate cancer, bone metastases, coronary artery disease,congenital heart defect, and Alzheimer's disease. Conventional systemsare used to detect lesions in a single image. These systems require theextraction of features which characterize the boundary shape of thelesion (morphology features) and the homogeneity of the grey levelwithin the lesion (texture features). The variation in size, shape,orientation, as well as ill-defined or diffused boundaries of lesionsand background noise makes it difficult to detect a true lesion fromother ultrasound artifacts found in the body.

Ultrasound medical imaging systems must address numerous artifacts whichcan make the detection of lesions more difficult. Generally ultrasoundartifacts can cause false detections and sometimes mask true detectionsof lesions. Common artifacts encountered in ultrasound imaging include:anisotropy, reverberation, acoustic shadowing, acoustic enhancement,edge shadowing, beam width artifact, slice thickness artifact, side lobeartifact, mirror image, double image, equipment-generated artifact, andrefraction artifact.

Anisotropy is the effect that makes a tendon appear bright when it runsat 90 degrees to the ultrasound beam, but dark when the angle ischanged. The reason for this is that at particularly smooth boundaries,the angle of reflection and incidence are the same, just as they arewith a conventional mirror. Reverberation is the production of falseechoes due to repeated reflections between two interfaces with highacoustic impedance mismatch. Acoustic shadowing on an ultrasound imageis characterized by a signal void behind structures that strongly absorbor reflect ultrasonic waves. Acoustic enhancement, also called posteriorenhancement or enhanced through transmission, refers to the increasedechoes deep to structures that transmit sound exceptionally well.Ultrasound beam width artifact occurs when a reflective object locatedbeyond the widened ultrasound beam, after the focal zone, creates falsedetectable echoes that are displayed as overlapping the structure ofinterest. Slice thickness artifacts are due to the thickness of the beamand are similar to beam width artifacts. Side lobe artefacts occur whereside lobes reflect sound from strong reflector that is outside of thecentral beam, and where the echoes are displayed as if they originatedfrom within the central beam. Mirror image artifact in ultrasonographyis seen when there is a highly reflective surface in the path of theprimary beam. Double image artifact is due to refraction of a regionlike a muscle which acts as a lens which generates a second image of areflector. Equipment-generated artifacts due to incorrect settings canlead to artifacts occurring. Refraction artifacts are due to sounddirection changes due to passing from one medium to another.

It is with these observations in mind, among others, that variousaspects of the present inventive concept were conceived and developed.

SUMMARY

It has been discovered that determining a persistence propertyadvantageously facilitates identification of true lesions from artifactsthat are detected in some views but are not consistent with the motionfield and/or block tracking information; and/or identification of truelesions from instantaneous anomalies that are only detected in a fewtemporal frames or appear at random locations that do not follow themotion field or tracking information. Accordingly, one implementation ofthe present inventive concept may take the form of a method, comprising:utilizing a computing device comprising a memory for storinginstructions that are executed by a processor to perform operations of:accessing a plurality of image frames; identifying a region of interestfrom a first image frame of the plurality of image frames; andprocessing the first image frame and a second image frame of theplurality of image frames to determine whether the region of interest isa false positive, by: comparing features of the first image frame withfeatures of the second image frame to determine if the region ofinterest persists across the first image frame and the second imageframe.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a method of detecting a lesion orabnormality using a computer-aided detection system. The method includesthe steps of collecting sequential image data, a video clip, volumetricset, and/or a sequence thereof and/or a combination thereof, collectingtemporal/sequential information associated with the image data, and/orprocessing the image data and the temporal/sequential data to detect adifference associated with the image data and the temporal/sequentialdata and reduce a number of false positive lesion or abnormalitydetections. The image data may be 2D image data. The method may includethe step of using at least one optical flow technique temporally toimprove performance of the system.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a computer-aided detection systemconfigured to identify a region-of interest (ROI) with a highprobability of containing a lesion or abnormality. The system mayinclude a processor configured to reduce a number of false positiveswhile preserving sensitivity or a number of true detections usingtemporal information. The temporal information may be determined usingoptical flow techniques. The system may include a correlation engineconfigured to determine correlations between ROIs found usingtraditional static CADe approaches for each image frame separately usingtracking information. The processor may be configured to measurepersistence as a number of frames that an ROI appears using the trackinginformation to determine false positives or low persistence and truepositives or high persistence. The processor may be configured tomeasure persistence by determining a degree of overlap of a predictedROI as given by a tracking motion vector. The ROI may be detected usingthe static CADe method. A greater degree of overlap may correspond to ahigher probability of a true lesion or lower probability of a falsepositive.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a system configured to detect a lesion orabnormality. The system may include a processor configured to receiveimage data and temporal information and/or a memory configured to storethe image data and the temporal information. The processor may beconfigured to process the image data and the temporal data to detect adifference associated with the image data and the temporal data andreduce a number of false positive lesion or abnormality detections.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a method of computer-aided detection toidentify a region-of-interest (ROI) with a high probability ofcontaining a lesion or abnormality, the method comprising the step of:using persistent spatial and/or temporal information associated with thetwo adjacent image frames to reduce a number of false positives whilepreserving or enhancing sensitivity or a number of true detections.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a method of detecting a lesion orabnormality using a computer-aided detection system, the methodcomprising the steps of: collecting image data, a video clip, and/or asequence; collecting temporal information associated with the imagedata; and processing the image data and the temporal data to detect adifference associated with the image data and the temporal data andreduce a number of false positive lesion or abnormality detections.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a computer-aided detection systemconfigured to identify a region-of-interest (ROI) with a highprobability of containing a lesion or abnormality, the systemcomprising: a processor configured to reduce a number of false positiveswhile preserving sensitivity or a number of true detections usingtemporal information.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a system configured to detect a lesion orabnormality, the system comprising: a processor configured to receiveimage data and temporal information; and a memory configured to storethe image data and the temporal information, wherein, the processor isconfigured to process the image data and the temporal data to detect adifference associated with the image data and the temporal data andreduce a number of false positive lesion or abnormality detections.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a method, comprising: utilizing acomputing device comprising at least one processing unit incommunication with at least one tangible storage media, the tangiblestorage media including computer executable instructions for performingoperations of: accessing sequential image frames associated withpredetermined time intervals; identifying a region of interestassociated with the sequential image frames; utilizing optical flow togenerate tracking or mapping information between the sequential imageframes; and generating a persistence value as a number of the sequentialimage frames that the region of interest appears or can be correlatedbetween certain ones of the sequential image frames using the trackingor mapping information.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing an apparatus, comprising: acomputer-aided detection (CAD) device operable to: utilize optical flowwith image frames to a determine a temporal persistence value of aregion of interest associated with the image frames over a predeterminedperiod of time.

BRIEF DESCRIPTION OF DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the office upon request and paymentof the necessary fee.

For the purposes of description, but not of limitation, the foregoingand other aspects of the present inventive concept are explained ingreater detail with reference to the accompanying drawings, in which:

FIG. 1A depicts an exemplary system for implementing aspects of thepresent inventive concept.

FIG. 1B depicts an exemplary system for implementing aspects of thepresent inventive concept.

FIG. 2A illustrates a Free-response Receiver Operating Characteristic(FROC) curve of detection of lesions that are relatively more difficultto identify, and showing the sensitivity of true positives on the y-axisas a function of the number of false positives per image on the x-axis,with a trade-off in number of false positives versus sensitivity or truepositive detection;

FIG. 2B illustrates a FROC curve of detection of lesions that arerelatively less difficult to identify, and showing the sensitivity oftrue positives on the y-axis as a function of the number of falsepositives per image on the x-axis, with a trade-off in number of falsepositives versus sensitivity or true positive detection;

FIGS. 3A-3D illustrates how false positives detected in images of FIGS.3A and 3C can be removed using one or more optical flow techniques ofthe present inventive concept to check on temporal persistence ofregions of interest detected, as illustrated by FIGS. 3B and 3D;

FIGS. 4A-4D illustrate detection results using optical flow shown ingreen (i.e., external squares and squares without any internal square)compared to ground truth, e.g., true lesion boundaries, manually markedand shown in red (i.e., internal squares);

FIGS. 5A and 5B illustrate detection results using optical flow shown ingreen (i.e., external squares and squares without any internal square)compared to ground truth manually marked in red (i.e., internalsquares);

FIGS. 6A and 6B illustrate detection results using optical flow shown ingreen compared to ground truth manually marked in red (i.e.,uppermost-extending squares);

FIGS. 7A and 7B illustrate detection results using optical flow shown ingreen compared to ground truth manually marked in red (i.e.,leftmost-extending squares);

FIGS. 8A and 8B illustrate detection results using optical flow shown ingreen compared to ground truth manually marked in red (i.e., leftmostand rightmost-extending squares);

FIG. 9 illustrates detection results using optical flow shown in greencompared to ground truth manually marked in red (i.e., uppermost andlowermost-extending square); and

FIG. 10 is an exemplary process flow for aspects of the presentinventive concept.

FIG. 11 is another exemplary process flow for aspects of the presentinventive concept.

The drawings do not limit the present inventive concept to the specificembodiments disclosed and described herein. The drawings are notnecessarily to scale, emphasis instead being placed on clearlyillustrating principles of certain embodiments of the present inventiveconcept.

DETAILED DESCRIPTION

The following detailed description references the accompanying drawingsthat illustrate various embodiments of the present inventive concept.The illustrations and description are intended to describe aspects andembodiments of the present inventive concept in sufficient detail toenable those skilled in the art to practice the present inventiveconcept. Other components can be utilized and changes can be madewithout departing from the scope of the present inventive concept. Thefollowing detailed description is, therefore, not to be taken in alimiting sense. The scope of the present inventive concept is definedonly by the appended claims, along with the full scope of equivalents towhich such claims are entitled.

I. Terminology

In the description, terminology is used to describe features of thepresent inventive concept. For example, references to terms “oneembodiment,” “an embodiment,” “the embodiment,” or “embodiments” meanthat the feature or features being referred to are included in at leastone aspect of the present inventive concept. Separate references toterms “one embodiment,” “an embodiment,” “the embodiment,” or“embodiments” in this description do not necessarily refer to the sameembodiment and are also not mutually exclusive unless so stated and/orexcept as will be readily apparent to those skilled in the art from thedescription. For example, a feature, structure, process, step, action,or the like described in one embodiment may also be included in otherembodiments, but is not necessarily included. Thus, the presentinventive concept may include a variety of combinations and/orintegrations of the embodiments described herein. Additionally, allaspects of the present inventive concept as described herein are notessential for its practice.

The term “algorithm” refers to logic, hardware, firmware, software,and/or a combination thereof that is configured to perform one or morefunctions including, but not limited to, those functions of the presentinventive concept specifically described herein or are readily apparentto those skilled in the art in view of the description. Such logic mayinclude circuitry having data processing and/or storage functionality.Examples of such circuitry may include, but are not limited to, amicroprocessor, one or more processors, e.g., processor cores, aprogrammable gate array, a microcontroller, an application specificintegrated circuit, a wireless receiver, transmitter and/or transceivercircuitry, semiconductor memory, or combinatorial logic.

The term “logic” refers to computer code and/or instructions in the formof one or more software modules, such as executable code in the form ofan executable application, an application programming interface (API), asubroutine, a function, a procedure, an applet, a servlet, a routine,source code, object code, a shared library/dynamic load library, or oneor more instructions. These software modules may be stored in any typeof a suitable non-transitory storage medium, or transitory storagemedium, e.g., electrical, optical, acoustical, or other form ofpropagated signals such as carrier waves, infrared signals, or digitalsignals. Examples of non-transitory storage medium may include, but arenot limited or restricted to a programmable circuit; a semiconductormemory; non-persistent storage such as volatile memory (e.g., any typeof random access memory “RAM”); persistent storage such as non-volatilememory (e.g., read-only memory “ROM”, power-backed RAM, flash memory,phase-change memory, etc.), a solid-state drive, hard disk drive, anoptical disc drive, or a portable memory device. As firmware, theexecutable code is stored in persistent storage.

The term “user” is generally used synonymously herein to represent auser of the system and/or method of the present inventive concept. Forpurposes herein, the user may be a clinician, a diagnostician, a doctor,a technician, a student, and/or an administrator.

The terms “identified,” “processed,” and “selected” are generally usedsynonymously herein, regardless of tense, to represent a computerizedprocess that is automatically performed or executed by the system in oneor more processes via at least one processor.

The acronym “CAD” means Computer-Assisted Diagnosis.

The term “client” means any program of software that connects to a CADlesion application.

The term “server” typically refers to a CAD lesion application that islistening for one or more clients unless otherwise specified.

The term “post-processing” means an algorithm applied to an inputtedultrasound image.

The acronym “PACS” means Picture Archival and Communication System.

The acronym “GSPS” means Grayscale Softcopy Presentation State.

The acronym “DICOM” means Digital Imaging and Communications inMedicine.

The acronym “UI” means User Interface.

The acronym “PHI” means Private Health Information.

The term “computerized” generally represents that any correspondingoperations are conducted by hardware in combination with software and/orfirmware.

Lastly, the terms “or” and “and/or” as used herein are to be interpretedas inclusive or meaning any one or any combination. Therefore, “A, B orC” or “A, B and/or C” mean “any of the following: A; B; C; A and B; Aand C; B and C; A, B and C.” An exception to this definition will occuronly when a combination of elements, functions, steps or acts are insome way inherently mutually exclusive.

As the present inventive concept is susceptible to embodiments of manydifferent forms, it is intended that the present inventive concept beconsidered as an example of the principles of the present inventiveconcept and not intended to limit the present inventive concept to thespecific embodiments shown and described.

II. General Architecture

A trained medical professional such as a radiologist will generallyattempt to identify and classify regions of suspicion or regions ofinterest within a medical image either manually or by using computersoftware. The radiologist may then manually characterize each region ofsuspicion in accordance with a relevant grading system. For example,suspicious regions of interest within the breast, which may includecancerous lesions, may be characterized according to Breast ImagingReporting and Data Systems (BI-RADS) guidelines.

Ultrasound technologists may be trained to move a transducer around aregion of interest (ROI) to obtain various viewpoints in order to reducethe uncertainty in detecting lesions due to artifacts and noise that cancause detection errors. A true lesion may be identified from differentviewpoints whereas most artifacts will change significantly with adifferent viewing angle. According to aspects of the present inventiveconcept, observation of various viewpoints of a region of interest maybe a major factor to the optimum detection of lesions by humanoperators. Conventional CADe systems generally look at only one image ortwo orthogonal images of a ROI to detect if a lesion is present in thefield of view (FOV). The present inventive concept contemplatesdetermining the portions of the ROI that persist over a plurality ofvarious viewing angles which can be analyzed by a CAD system asdescribed herein in real-time or offline.

Aspects of the present inventive concept comprise a system and method toprocess medical images using optical flow and block matching (ortracking) methods, or by generating a mapping function of ROIs betweenadjacent images in a sequence. A mapping function of the presentinventive concept may involve amplitude and vector mappings to determinemotion or tracking information between consecutive temporal frames ofmedical images from varying viewpoints.

Optical flow or optic flow may be described as a pattern of objects,surfaces, edges, or other visual characteristics of a given image (orset of images) taking into account relative motion associated with theimage. Optical flow methods may be used to calculate the motion betweentwo image frames based on time intervals. Using sets of images, insequence, motion may be estimated as image velocities or discrete imagedisplacements. Optical flow methods as described herein may includephase correlation, block-based methods, differential methods, discreteoptimization methods, and the like. In one embodiment, block basedmethods may be utilized to minimize the sum of squared differences orsum of absolute differences, or to maximize normalized cross-correlationassociated with adjacent images in a sequence. Tracking methods may alsobe utilized. Specifically, feature-tracking may be utilized whichcomprises the extraction of visual features such as corners and texturedareas and tracking them over multiple frames. As one example ofimplementing feature tracking, given two subsequent frames, pointtranslation can be estimated.

The mapping or tracking information may be used to determine whetherimage frames can be correlated based on regions of interest (ROIs) foundin each temporal frame using static two dimensional (2D) CADeapproaches. Frames of an image or multiple images can also be simulatedas a sequence derived from sequential traverses through arbitrary anglesin volumetric whole breast ultrasound data. Using the system and methodof the present inventive concept, a true lesion may appear as an ROIthat can be tracked over several viewpoints. On the other hand, falsepositives may be shown to persist for a relatively few number ofviewpoints and appear at locations that are not correlated to theoptical flow and block matching tracking information of the ROI.

As such, the present inventive concept overcomes the limitation ofcurrent CADe systems by processing adjacent images from multiple viewsof a region of interest, i.e., a plurality of images of a region ofinterest with each of the plurality of images being of a different viewof the region of interest. It is foreseen that the system and method ofthe present inventive concept may utilize multiple images of the sameview of the region of interest without deviating from the scope of thepresent inventive concept. The plurality of images may be used to reducethe interfering effects of artifacts in the computer assisted detectionCADe of lesions.

The present inventive concept advantageously utilizes informationpertaining to a multitude of viewpoints obtained from varying thetransducer angle or simulating sequences from volumetric whole breastultrasound data to improve the performance over current single, double,or volumetric viewpoint image CADe systems. For example, a sequence ofimages for a specific region of interest may be processed to finddisplacement vectors of patches of the image from one image to the nextin the sequence. Patches are defined as having similar sets ofmorphological and texture features within the patch across the sequenceof images. The resulting displacement vectors and average grey scalechange of a patch throughout a sequence are used to increase or decreasethe probability of detection of a lesion using the CADe for the ROI.

Further, the persistence of the ROI between images may be used toprovide a confidence level of the lesion detected from a static CADesystem. The operator may record images for CADx diagnosis at and aroundthe regions of interest with the highest confidence levels.

FIG. 1A illustrates an exemplary CAD system 20 which may be utilized toperform an image analysis process comprising one or more stages. Asshown, the CAD system 20 may comprise a radiology workstation 22, aDICOM web viewer access 24, a PACS server 26, and a CAD device 28. Theradiology workstation 22 may comprise at least one high-definitionmonitor, an adjustable clinician/operator desk, a power supply, adesktop computer or other such computing device, cable peripherals, apower supply, and PACS-specific peripheral such as a PACS back light,and PACS monitor device holders/frames.

The PACS server 26 may comprise a system for digital storage,transmission, and retrieval of medical images such as radiology images.The PACS server 26 may comprise software and hardware components whichdirectly interface with imaging modalities. The images may betransferred from the PACS server 26 to external devices for viewing andreporting. The CAD device 28 may access images from the PACS server 26or directly from the radiology workstation 22.

The CAD device 28, which may comprise a CAD web and processing server orother CAD computing device may comprise at least one of an applicationserver, web server, processing server, network server, mainframe,desktop computer, or other computing device. The CAD device 28 mayfurther comprise at least one Windows based, tower, or rack form factorserver. The CAD device 28 may be operable to provide a client side userinterface implemented in JavaScript, HTML, or CSS. The CAD device 28 maycomprise a server side interface implemented in e.g. Microsoft ASP/.NET,C#/PHP, or the like. The CAD device 28 may utilize one or more cloudservices to extend access and service to other devices, such as theradiology workstation 22.

The CAD device 28 may communicate with the radiology workstation 22 viaa network using the DICOM web viewer access 24, a web interface, orusing one or more of an application programming interface (API). TheDICOM web viewer access 24 may comprise a medical image viewer and mayrun on any platform with a modern browser such as a laptop, tablet,smartphone, Internet television, or other computing device. It isoperable to load local or remote data in DICOM format (the standard formedical imaging data such as magnetic resonance imaging (MRI),computerized tomography (CT), Echo, mammography, etc.) and providesstandard tools for its manipulations such as contrast, zoom, drag,drawing for certain regions of images, and image filters such asthreshold and sharpening. In one embodiment, the radiology workstation22 may communicate with the CAD device 28 by implementing the DICOM webviewer access 24 via a web browser of a computing device of theradiology workstation 22. It should be understood that aspects of thepresent inventive concept may be implemented solely on the CAD device 28which may already have access to one or more medical images.

The CAD device 28 may implement aspects of the present inventive conceptusing a CAD application 12 developed in C++, but other programminglanguages are contemplated. The CAD application 12 may be compatiblewith and utilize aspects of an operating system such as Windows XP,Windows Vista, Windows 7, Windows 8, Windows 10, and their embeddedcounterparts. The CAD lesion application 12 may be hardware agnostic andmay be implemented on a variety of different computing devices such asapplication servers, network servers, mainframes, desktop computers, orthe like. The CAD application 12 may utilize an external interface whichmay be accessed by a user, such as a clinician.

The CAD application 12 may be installed to or otherwise reside on anoperating system of a computing device, such as the CAD device 28. TheCAD application 12 may comprise a self-contained application which neednot rely upon any core functionality outside of the operating systemwithin which it resides. The CAD application 12 may include a DICOMinterface that allows the CAD lesion application 12 to receive DICOMdata as well as return such data to the originator of the transaction.

FIG. 1B is another embodiment of a system 100 for implementing imageprocessing including performing an image analysis process comprising oneor more stages similar to the system 20 of FIG. 1A as described herein.As shown, the system 100 comprises an image generating device 102, a setof images 104 or image frames, and a computing device 106 for processingthe images 104. It should be understood that the system 20 and system100 are not mutually exclusive such that the inventive concept of CADprocessing as described herein may involve features from the system 20and/or the system 100.

FIGS. 1A and 1B may be explained with reference to the process flow 1000of FIG. 10. In block 1002, a plurality of image frames may be accessedby a computing device. The computing device of FIG. 10 may be the CADdevice 28, or the computing device 106. The computing device 106 is notlimited to a CAD device and may be a desktop, laptop, or server or amobile device. The image frames may be generated using the imagegenerating device 102 which may include any number or type of imagegenerating devices such as a transducer, a 3-D ultrasound, a fluoroscopydevice using continuous X-ray beams, or the like. The image frames orimages are depicted as the images 104 of FIG. 1B as generated using theimage generating device 102. The images 104 may be taken over a temporalrange, or may be generated based on spatial relationships and location.For example, an operator may move a transducer slightly over an area ofa patient during a time period, and many of the images 104 may begenerated during the time period, such that each of the images isassociated with a predetermined time interval. In other embodiments, forexample in the case of 3-D ultrasound, a plurality of images such as theimages 104 may be taken nearly instantaneously. In this case, the imagesmay be associated with a unique spatial identifier, i.e., the images maybe associated with specific locations or sections of a patient asopposed to specific time intervals. As described herein, if a region ofinterest is determined to persist across multiple image frames over timeor spatially, the ROI may be ruled out as a false positive.

As shown in block 1004, a region of interest is identified from a firstimage frame of the plurality of image frames of block 1002. In otherwords, block 1004 may comprise a stage of an image process/analysis thatmay include automatically detecting a region-of-interest (ROI) from theimages 104 which might be a lesion or abnormality that requires furtherevaluation. This stage may be denoted CADe, where the subscript eindicates the detection of a putative lesion or abnormality within aregion of interest. The plurality of image frames may be accessed from aPACS server such as the PACS server 26, or generated by the imagegenerative device 102.

As described in block 1006, a computing device, such as the computingdevice 106 may be used to process the first image frame and other imageframes of the images 104 to determine whether the region of interest(ROI) is a false positive or requires further analysis. Moreparticularly, if an ROI is identified in block 1004 that may contain alesion or anomaly, a CADx process/stage may be executed. In the CADxstage, the ROI is analyzed to determine the likelihood that the lesioncontained therein is cancerous or benign. The subscript x indicatesdiagnosis. As shown in block 1008, the processing involves comparingfeatures of the first image frame with features of other image frames todetermine if the region of interest persists across or between multipleframes.

Processing image frames as described in block 1006 and block 1008, usinga CADx stage, may involve a variety of different possible sub-methodssuch as optical flow, block based methods, mapping functions, or thelike as described herein. For example, at least one processer of the CADdevice 28 or the computing device 106 may be used to compute mappingbetween temporal/sequential frames via optical flow techniques and blockmatching to determine tracking information of objects between frames.Independent ROIs may be identified using static CADe techniques thatprocess one image per viewpoint of the sequence. The trackinginformation may then be used to determine whether ROIs across frames arecorrelated to a persistent ROI. A true lesion or abnormality shouldpersist over many frames whereas a false positive will not. The numberof frames can be adapted by a user either manually or automatically,e.g., via an interface of a computing device or the CAD device 28, tocontrol the reduction in false positives at the expense of lowering thetrue positive rate.

The degree of overlap of the ROIs, using the tracking informationobtained with optical flow methods, can also be used to determinewhether the lesion is the same between frames or if it is just two falsepositives. The degree of overlap can be adjusted in order to reduce morefalse positives or increase the number of true detections. Theperformance of the methods described herein has been found to reduce thenumber of false positives while keeping the same true positive rate thatwas achieved using static CADe approaches.

In one embodiment, to implement aspects of block 1006 and block 1008,image frames may be correlated with the first image frame by analyzingpixels and vectors associated with the region of interest. In otherwords, image frames may be correlated with the first image frame wherethe image frames and the first image frame depict similar pixels andvectors moving in the same direction.

It is contemplated that lesion morphology, texture, and other featuresextracted from images using the CAD system 20 or the system 100 may beused as features to train a classifier to detect putative lesions.Further, it is contemplated that static methods may be used to detectputative lesions for individual frames of video or still medical images.For purposes herein, the static methods are CADe methods that mayprocess on one independent, two orthogonal image viewpoints, or somesubset of captured or recapitulated images from a volumetric dataset.CADe methods are not limited to morphology and texture, but include anymethod that searches for suspicious areas within one image. For example,cascading Adaboost methods like the Viola-Jones method, convolutionalneural networks (CNN), support vector machines (SVM) using Haralick andother features are techniques that may be used for static CADe methods.Using the CAD system 20 or system 100 and methods described herein withdynamic information available in real-time medical imaging applications,e.g., ultrasound imaging systems, performance of static CADe systems maybe enhanced. Optical flow or optic flow is the pattern of apparentmotion of objects, surfaces, and edges in a visual scene. Sequences ofordered images allow the estimation of motion as either instantaneousimage velocities or discrete image displacements. Optical flow methodsare divided into gradient-based or feature-based methods and can usestochastic or deterministic models of the objects whose motion is beingestimated. Optical flow algorithms include but are not limited to phasecorrelation, block-based methods, differential methods like Lucas-Kanademethod, Horn-Schunck method, Buxton-Buxton method, Black-Jepson method,general variational methods and discrete optimization methods. Blockmatching methods may be described here track grey scale changes inpatches of pixels between images in a sequence.

In one embodiment, system and methods of the present inventive conceptmay advantageously calculate ROIs separately for each frame using astatic-based CADe approach. The system and method of the presentinventive concept could use the preferred method of optical flow methodsto determine tracking information between consecutive frames in a videosequence of a ROI taken from various viewpoints. Specifically, thetracking information may be used to determine whether the ROI obtainedindependently in each frame using static CADe approaches can becorrelated to each other using the optical flow vector field.Persistence is measured as the number of frames that an ROI can betracked based on a percentage of overlap of the ROIs. The persistencefactor is use to filter out putative lesions that have low persistence.Any ROI that is not tracked through several frames using optical flowmethods may be determined or deemed to be a false positive. Thepersistence factor or number of frames can be adapted to reduce morefalse positives at the expense of a greater probability of a missedlesion. The degree overlap of the ROIs based on the tracked predictionand actual ROI location can also be adapted to reduce the number offalse positives or increase the number of true positives. Theaforementioned may be achieved in one aspect of the present inventiveconcept by providing a method of computer-aided detection to identify aregion-of-interest (ROI) with a high probability of containing a lesionor abnormality.

In some embodiments, exemplary methods of utilizing a CAD system asdescribed herein may include the step of using temporal information toreduce a number of false positives while preserving sensitivity or anumber of true detections. The temporal information may be determinedusing optical flow techniques.

Exemplary methods may further include the step of determiningcorrelations between ROIs found using traditional static CADe approachesfor each image frame separately using tracking information. Exemplarymethods may further include the step of measuring persistence as anumber of frames that an ROI appears using the tracking information todetermine false positives or low persistence and true positives or highpersistence. Exemplary methods may further include the step of measuringpersistence by determining a degree of overlap of a predicted ROI asgiven by a tracking motion vector. The present inventive concept can beimplemented in real-time or on recorded video such as a cineloop. It isforeseen that any captured images and/or video by the present inventiveconcept may be two-dimensional images and/or video such as, but notlimited to a cineloop. For instance, a cineloop, as used in anultrasound procedure, may be incorporated into the process and/or methodof the present inventive concept. Moreover, it is foreseen that at leastone or more portions and preferably all portions of the systems andmethods of the present inventive concept utilize and comply with DigitalImaging and Communications in Medicine (DICOM) format standards. In thismanner, the system and methods of the present inventive concept utilizeDICOM file format definitions, network communications protocols, and thelike.

In addition to determining inter-frame correspondences andrelationships, the optical flow methods described herein can be used tocharacterize tissue response to shear and compressive forces. Theseforces may be imparted by the operator applying pressure on thetransducer, as they move it over the course of an examination. It is awell-known fact that, as a result of changes in cell density, certaintypes of lesions may exhibit differing levels of stiffness anddeformability. The system described herein, in tracking the position andshape of a region of interest across spatial or temporal frames, mayalso infer the compressibility of that region with respect to itssurroundings. It may then use this characterization to aid itsdetermination of whether that region corresponds to abnormal or normaltissue.

Aspects of the present inventive concept may utilize one or more opticalflow sensors to generate mapping or tracking information between imagesas described herein. An optical flow sensor may comprise a vision sensoroperable to measure optical flow or visual motion and output ameasurement. Various embodiments of an optical sensor as described mayinclude an image sensor chip coupled to a processor with the processoroperable to execute an optical flow application. Other embodiments mayinclude a vision chip as an integrated circuit with an image sensor anda processor disposed on a common die or like component to increasedensity and reduce space.

In another embodiment, a process of temporally analyzing featureproperties may be implemented (alone or in combination with the above)to improve detection accuracy. Determining a persistence property mayfacilitate to identify true lesions from instantaneous anomalies thatare only detected in a few temporal frames or appear at random locationsthat do not follow the motion field or tracking information.Specifically, optical flow methods similar to those described above maybe utilized to find a mapping function. Mapping or tracking informationmay be used to determine whether the tracking information can becorrelated with regions of interest (ROIs) found in each temporal frame(or at least a plurality of temporal frames) using static (2D) CADeapproaches. A true lesion may be identified as an ROI that can betracked over many frames, with the understanding that false positivesgenerally persist for very few frames and appear at random locationsthat are not correlated to the optical flow tracking information. Inother words, the present novel concept advantageously utilizes temporalinformation to improve the performance of any static CADe system. Themapping function of the present inventive concept may include amplitudeand vector mappings to determine motion or tracking information betweenconsecutive temporal frames of medical images.

The present inventive concept may involve utilizing a CAD system, suchas the CAD system 20, to perform an image analysis process which mayinclude two stages. A first stage of the process may be to automaticallydetect a region-of-interest (ROI) which might be a lesion or abnormalitythat requires further evaluation. This stage may be denoted CADe, wherethe subscript e indicates the detection of a putative lesion orabnormality within a region of interest. If an ROI is identified thatmight contain a lesion or anomaly, then a next stage of the process,i.e., CADx, may be automatically executed. In the CADx stage, the ROI isanalyzed to determine the likelihood that the lesion contained thereinis cancerous or benign. The subscript x indicates diagnosis.

Using the systems described, computer-aided detection of medical imagesmay be conducted to detect the temporal persistence of ROIs that areautomatically detected in sequential video frames. Detection of temporalpersistence of ROIs may be used to enhance the probability of detectionof a true lesion or other abnormality while reducing the number of falsepositives. In one embodiment, the present inventive concept utilizesmapping between temporal frames via optical flow techniques to determinetracking information of objects between frames. Independent ROIs may beidentified using static CADe techniques that process one frame at a timewithout any temporal information. The tracking information is used todetermine whether ROIs across frames are correlated to the trackedgroup. A true lesion or abnormality should persist over many frameswhereas a false positive will not. The number of frames can be adaptedby a user either manually or automatically, e.g., via an interface ofthe present inventive concept and/or predetermined configurations, tocontrol the reduction in false positives at the expense of lowering thetrue positive rate. The degree of overlap of the ROIs using the trackinginformation obtained with optical flow methods can also be used todetermine whether the lesion is the same between the frames or if it isjust two false positives. The degree of overlap can be adjusted in orderto reduce more false positives or increase the number of truedetections. The performance of the instant new dynamic method has beenfound to reduce the number of false positives while keeping the sametrue positive rate that was achieved using static CADe approaches.

It is foreseen that lesion morphology, texture and other features forclassification, some of which may be commonly used features, may be usedas features to train a classifier to detect putative lesions in thiscapacity. Further, it is foreseen that static methods may be used todetect putative lesions for individual frames of video or still medicalimages. For purposes herein, the static methods may include CADemethods. CADe methods are not limited to morphology and texture, butinclude any method that searches for suspicious areas within one image.For example, cascading Adaboost methods like the Viola-Jones method,convolutional neural networks (CNN), support vector machines (SVM) usingHaralick and other features are techniques that may be used for staticCADe methods. Using the system and method of the present inventiveconcept with dynamic information available in real-time medical imagingapplications, e.g., ultrasound imaging systems, performance of staticCADe systems may be heightened.

Optical flow or optic flow is the pattern of apparent motion of objects,surfaces, and edges in a visual scene. Sequences of ordered images allowthe estimation of motion as either instantaneous image velocities ordiscrete image displacements. Optical flow methods are divided intogradient-based or feature-based methods and can use stochastic ordeterministic models of the objects whose motion is being estimated.Optical flow algorithms include but are not limited to phasecorrelation, block-based methods, differential methods like Lucas-Kanademethod, Horn-Schunck method, Buxton-Buxton method, Black-Jepson method,general variational methods and discrete optimization methods.

In one embodiment, the system and method of the present inventiveconcept advantageously utilizes a matching process between sequentialframes of video images to improve the probability of detecting a truelesion in a detected ROI. The system and method of the present inventiveconcept advantageously utilizes optical flow methods to matchsub-regions of a ROI within one image with similar sub-regions ofanother image.

In one embodiment, the system and method of the present inventiveconcept advantageously calculates ROIs separately for each frame using astatic-based CADe approach. The system and method of the presentinventive concept may further utilize optical flow methods to determinetracking information between consecutive frames in a video sequence. Thetracking information is used to determine whether ROIs obtainedindependently in each frame using static CADe approaches can becorrelated to each other using the optical flow vector field.Persistence is measured as the number of frames that an ROI can betracked based on a percentage of overlap of the ROIs. The persistencefactor is use to filter out putative lesions that have low persistence.Any ROI that is not tracked through several frames using optical flowmethods is determined to be a false positive. The persistence factor ornumber of frames can be adapted to reduce more false positives at theexpense of a greater probability of a missed lesion. The degree overlapof the ROIs based on the tracked prediction and actual ROI location canalso be adapted to reduce the number of false positives or increase thenumber of true positives.

The aforementioned may be achieved in one aspect of the presentinventive concept by providing a method of computer-aided detection toidentify a region-of-interest (ROI) with a high probability ofcontaining a lesion or abnormality. The method may include the step ofusing temporal information to reduce a number of false positives whilepreserving sensitivity or a number of true detections. The temporalinformation may be determined using optical flow techniques.

Methods may include the step of determining correlations between ROIsfound using traditional static CADe approaches for each image frameseparately using tracking information. The method may include the stepof measuring persistence as a number of frames that an ROI appears usingthe tracking information to determine false positives or low persistenceand true positives or high persistence. The method may further includethe step of measuring persistence by determining a degree of overlap ofa predicted ROI as given by a tracking motion vector.

It is foreseen that any captured images and/or video by the presentinventive concept may be two-dimensional images and/or video such as,but not limited to a cineloop. For instance, a cineloop, as used in anultrasound procedure, may be incorporated into the process and/or methodof the present inventive concept. Moreover, it is foreseen that at leastone or more portions and preferably all portions of the systems andmethods of the present inventive concept utilize and comply with DigitalImaging and Communications in Medicine (DICOM) format standards. In thismanner, the system and methods of the present inventive concept utilizeDICOM file format definitions, network communications protocols, and thelike.

The ROI may be detected using the static CADe method. A greater degreeof overlap may correspond to a higher probability of a true lesion orlower probability of a false positive.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a method of detecting a lesion orabnormality using a computer-aided detection system. The method includethe steps of collecting image data, a video clip, and/or a sequencethereof and/or a combination thereof, collecting temporal informationassociated with the image data, and/or processing the image data and thetemporal data to detect a difference associated with the image data andthe temporal data and reduce a number of false positive lesion orabnormality detections. The image data may be 2D image data. The methodmay include the step of using at least one optical flow techniquetemporally to improve performance of the system.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a computer-aided detection systemconfigured to identify a region-of-interest (ROI) with a highprobability of containing a lesion or abnormality. The system mayinclude a processor configured to reduce.

A system of the present disclosure may include a correlation engine,executed by a processor (of e.g. CAD device 28) configured to determinecorrelations between ROIs found using traditional static CADe approachesfor each image frame separately using tracking information. Theprocessor may be configured to measure persistence as a number of framesthat an ROI appears using the tracking information to determine falsepositives or low persistence and true positives or high persistence. Theprocessor may be configured to measure persistence by determining adegree of overlap of a predicted ROI as given by a tracking motionvector. The ROI may be detected using the static CADe method. A greaterdegree of overlap may correspond to a higher probability of a truelesion or lower probability of a false positive.

The aforementioned may be achieved in another aspect of the presentinventive concept by providing a system configured to detect a lesion orabnormality. The system may include a processor configured to receiveimage data and temporal information and/or a memory configured to storethe image data and the temporal information. The processor may beconfigured to process the image data and the temporal data to detect adifference associated with the image data and the temporal data andreduce a number of false positive lesion or abnormality detections.

In sum, computer-aided detection (CAD or CADe) systems may be used todetect lesions or regions of interest (ROIs) in medical images. CADsystems may use morphology and texture features for estimating thelikelihood of an ROI containing a lesion within an image. The largevariation in the size, shape, orientation, as well as ill-definedboundaries of lesions and background noise makes it difficult todifferentiate actual lesions from normal background structures or tissuecharacteristics. Conventional detection methods may generate many falsepositives or a detection of a lesion when one is not present when thesystem is set for an acceptable number of false negatives or missedlesions. In mammography CAD, it is usual to have thousands of falsepositives for every one true detected cancer making the systeminefficient for radiologists to use. The present inventive conceptdescribed overcomes this limitation by using optical flow methods toidentify ROIs that are detected with persistence over time. The propertyof persistence of a potential lesion helps reduce false positives aswell as improves the probability of detection of true lesions. This newdynamic method offers improved performance over static CADe methods forautomatic ROI lesion detection.

FIG. 11 is another exemplary process flow 1100 for implementing aspectsof the present inventive concept, similar to the process flow 1000 ofFIG. 10. As shown in block 1102, a CAD device may be implemented toperform certain functions. Specifically, in block 1104, the CAD devicemay access sequential image frames associated with predetermined timeintervals or spatial areas of a patient. In block 1106, a region ofinterest may be identified within one or more of the sequential imageframes. In block 1108, optical flow, or mapping functions may beimplemented to process the sequential image frames and determine whetherthe region of interest persists between one or more of the image framesin the sequence. In block 1110, a persistence value may be generated. Asshown in block 1112, the persistence value is based on a degree ofoverlap of the region of interest between frames of the sequential imageframes.

A display device 108 may further be implemented with the computingdevice 106 to process images, or display the images 104 afterprocessing. In some embodiments, the display device is directly coupledto the computing device 106 or the devices are part of the same device.

Additional aspects, advantages, and utilities of the present inventiveconcept will be set forth in part in the present description anddrawings and, in part, will be obvious from the present description anddrawings, or may be learned by practice of the present inventiveconcept. The present description and drawings are intended to beillustrative and are not meant in a limiting sense. Many features andsub-combinations of the present inventive concept may be made and willbe readily evident upon a study of the present description and drawings.These features and sub-combinations may be employed without reference toother features and sub-combinations.

What is claimed is:
 1. A method, comprising: utilizing a computingdevice comprising a memory for storing instructions that are executed bya processor to perform operations of: accessing a plurality of imageframes; identifying a region of interest from a first image frame of theplurality of image frames; and processing the first image frame and asecond image frame of the plurality of image frames to determine whetherthe region of interest is a false positive, by: comparing features ofthe first image frame with features of the second image frame todetermine if the region of interest persists across the first imageframe and the second image frame.
 2. The method of claim 1, furthercomprising: generating the plurality of image frames within apredetermined temporal range such that the plurality of image framescomprise a sequence of image features temporally close together.
 3. Themethod of claim 1, further comprising: generating the plurality of imageframes within a predetermined spatial range such that the plurality ofimage frames are in close proximity to one another.
 4. The method ofclaim 1, further comprising: comparing the features of the first imageframe with the features of the second image frame by utilizing a blockbased method to minimize a sum of squared differences or sum of absolutedifferences, or to maximize normalized cross-correlation associated withthe optical flow or motion between the features of the first image frameand the features of the second image frame.
 5. The method of claim 1,further comprising: comparing the features of the first image frame withthe features of the second image frame by: tracking a position and shapeof the region of interest; and inferring a compressibility of the regionof interest with respect to surrounding features to determine whetherthe region of interest corresponds to abnormal or normal tissue.
 6. Themethod of claim 1, further comprising: generating the plurality of imagefeatures using a transducer, ultrasound, or fluoroscopy; and wherein thecomputing device comprises a computer-assisted diagnosis device.
 7. Themethod of claim 1, further comprising: comparing the features of thefirst image frame with the features of additional image frames of theplurality of image frames, by: identifying a group of pixels associatedwith the region of interest; generating a first vector associated withthe region of interest; determining a motion of the group of pixels; andgenerating a second vector associated with the motion of the group ofpixels.
 8. The method of claim 1, further comprising: comparing thefeatures of the first image frame with features of additional imageframes of the plurality of image frames, by: utilizing image velocitiesor discrete image displacements associated with the features of thefirst image frame and the features of the second image frame.
 9. Themethod of claim 1, further comprising: comparing the features of thefirst image frame with features of additional image frames of theplurality of image frames, by: measuring a number of image frames of theplurality of image frames where the region of interest is present,determining that region of interest is the false positive where thenumber of image frames of the plurality of image frames where the regionof interest is present is below a predetermine value.
 10. A method ofcomputer-aided detection to identify a region-of-interest (ROI) with ahigh probability of containing a lesion or abnormality, the methodcomprising the step of: using persistent spatial and/or temporalinformation associated with the two adjacent image frames to reduce anumber of false positives while preserving or enhancing sensitivity or anumber of true detections.
 11. The method of claim 10, furthercomprising the step of: determining persistent spatial and/or temporalinformation using one or more optical flow techniques.
 12. The method ofclaim 10, further comprising the step of: determining one or morecorrelations between ROIs found using traditional static CADe approachesfor each image frame separately using tracking information.
 13. Themethod of claim 12, further comprising the step of: measuringpersistence as a number of frames that an ROI appears using the trackinginformation to determine false positives as having a low persistence andtrue positives as having a high persistence.
 14. The method of claim 12,further comprising the step of: measuring persistence by determining adegree of overlap of a predicted ROI as given by a tracking motionvector.
 15. The method of claim 14, wherein, the ROI is detected usingthe static CADe method.
 16. The method of claim 9, wherein, a greaterdegree of overlap corresponds to a higher probability of a true lesionor lower probability of a false positive.
 17. A method of detecting alesion or abnormality using a computer-aided detection system, themethod comprising the steps of: collecting image data, a video clip,and/or a sequence; collecting temporal information associated with theimage data; and processing the image data and the temporal data todetect a difference associated with the image data and the temporal dataand reduce a number of false positive lesion or abnormality detections.18. The method of claim 17, wherein, the image data is 2D image data.19. The method of claim 13, further comprising the step of: using atleast one optical flow technique temporally to improve performance ofthe system.
 20. A computer-aided detection system configured to identifya region-of-interest (ROI) with a high probability of containing alesion or abnormality, the system comprising: a processor configured toreduce a number of false positives while preserving sensitivity or anumber of true detections using temporal information.
 21. The system ofclaim 20, wherein, the temporal information is determined using one ormore optical flow techniques.
 22. The system of claim 20, furthercomprising: a correlation engine configured to determine correlationsbetween ROIs found using traditional static CADe approaches for eachimage frame separately using tracking information.
 23. The system ofclaim 20, wherein, the processor is configured to measure persistence asa number of frames that an ROI appears using the tracking information todetermine false positives or low persistence and true positives or highpersistence.
 24. The system of claim 20, wherein, the processor isconfigured to measure persistence by determining a degree of overlap ofa predicted ROI as given by a tracking motion vector.
 25. The system ofclaim 22, wherein, the ROI is detected using the static CADe method. 26.The system of claim 22, wherein, a greater degree of overlap correspondsto a higher probability of a true lesion or lower probability of a falsepositive.
 27. A system configured to detect a lesion or abnormality, thesystem comprising: a processor configured to receive image data andtemporal information; and a memory configured to store the image dataand the temporal information, wherein, the processor is configured toprocess the image data and the temporal data to detect a differenceassociated with the image data and the temporal data and reduce a numberof false positive lesion or abnormality detections.
 28. The system ofclaim 27, wherein, the image data is 2D image data.
 29. The system ofclaim 28, wherein, the processor is configured to use at least oneoptical flow technique temporally to improve performance of the system.30. The system of claim 24, further comprising the steps of: inferring,via the processor, compressibility from one or more vector fieldsproduced by optical flow calculations; and forming a diagnosisconclusion by utilizing the compressibility from the one or more vectorfields produced by the optical flow calculations.
 31. A method,comprising: utilizing a computing device comprising at least oneprocessing unit in communication with at least one tangible storagemedia, the tangible storage media including computer executableinstructions for performing operations of: accessing sequential imageframes associated with predetermined time intervals; identifying aregion of interest associated with the sequential image frames;utilizing optical flow to generate tracking or mapping informationbetween the sequential image frames; and generating a persistence valueas a number of the sequential image frames that the region of interestappears or can be correlated between certain ones of the sequentialimage frames using the tracking or mapping information.
 32. The methodof claim 31, wherein, the persistence value is based on a predetermineddegree of overlap of the region of interest between frames of thesequential image frames as given by a tracking motion vector, thepredetermined degree of overlap being adjustable to reduce more falsepositives or increase a number of true detections.
 33. The method ofclaim 31, wherein, generating the persistence value includes determiningwhether a first region of interest associated a first frame of thesequential image frames can be correlated with a second region ofinterest associated with a second frame of the sequential image framesusing an optical flow vector field.
 34. The method of claim 31, furthercomprising: identifying the region of interest as associated with thesequential image frames using a static two dimensional CADe approachthat detects the region of interest within each of at least a portion ofthe sequential image frames one at a time, the region of interestassociated with a possible lesion or abnormality that needs furtherevaluation.
 35. The method of claim 31, further comprising: generating afunction using the optical flow to generate the tracking or mappinginformation, wherein, the function includes amplitude and vectormappings to determine motion or tracking information between consecutivetemporal frames of the sequential image frames, the sequential imageframes comprising consecutive temporal frames of medical images.
 36. Themethod of claim 31, further comprising: adapting a number of thesequential image frames via an interface coupled to the computing deviceto control a reduction in false positives at an expense of reducing atrue positive rate.
 37. An apparatus, comprising: a computer-aideddetection (CAD) device operable to: utilize optical flow with imageframes to a determine a temporal persistence value of a region ofinterest associated with the image frames over a predetermined period oftime.
 38. The apparatus of claim 37, wherein, the persistence value ismeasured as a number of the image frames that the region of interest canbe tracked based on a percentage of overlap of the region of interest ofthe number of the image frames.
 39. The apparatus of claim 37, wherein,the CAD device is configured to match sub-regions of a first region ofinterest associated with a first image frame of the image frames withsimilar sub-regions of a second image frame of the image frames.
 40. Theapparatus of claim 37, wherein, the CAD device is utilized to train aclassifier to detect the region of interest based on image featuresassociated with putative lesions.
 41. The apparatus of claim 37,wherein, the CAD device is configured to generate mapping or trackinginformation from the optical flo, and the CAD device is configured todistinguish the region of interest from a false positive image featurewhere the false positive image feature does not persist over at least apredetermined number of the image frames over the predetermined periodof time.
 42. The apparatus of claim 37, wherein, the CAD device isconfigured to utilize temporal persistence of regions of interest thatdetected in sequential video frames to enhance a probability ofdetection of a true lesion while reducing a number of false positives.43. The apparatus of claim 37, wherein, the CAD device is configured tomap between temporal frames of the image frames via optical flowtechniques to determine tracking information of objects between thetemporal frames.
 44. The apparatus of claim 37, wherein, the imageframes include images or video, and the image frames are accessed from astorage device and comprise Digital Imaging and Communications inMedicine (DICOM) file format definitions.
 45. The method of claim 1,wherein the region of interest is identified by a computer assistdetection CADe method.
 46. The method of claim 45, wherein the region ofinterest identified by the CADe method is edited by an operator so as toidentify and label false from true detection made by the CADe method.47. The method of claim 45, wherein the operator edited and labeleddetentions are used to re-train the CADe method to decrease theprobability of generating false positives.
 48. The method of claim 45,further comprising identifying additional regions of interest notdetected by the CADe method.
 49. The method of claim 48, wherein theoperator edited and labeled detentions are used to re-train the CADemethod to decrease the probability of false negatives.
 50. The method ofclaim 47, wherein the CADe method is re-trained using an aggregation ofdata collected from multiple operators.
 51. The method of claim 49,wherein the CADe method is re-trained using an aggregation of datacollected from multiple operators.
 52. The method of claim 51, furthercomprising providing feedback to the operator regarding a decisionwhether the region of interest is a true region of interest associatedwith a lesion.